维持性血液透析病人容量管理行为影响因素及预测模型

Prediction model and influencing factors of capacity management behaviorin maintenance hemodialysis patients

目的:调查维持性血液透析(MHD)病人容量管理行为现状,探讨相关影响因素。方法:共收集115例病人临床相关资料,对病人进行问卷资料收集,包括一般资料、容量管理行为、自我效能、健康素养等,分析MHD病人容量管理行为现状,以及维持性血透病人容量管理行为的影响因素,并建立容量管理行为预测模型。结果:115例MHD病人容量管理行为总分(18.47±2.66)分。血液透析相关指标得分为(12.24±1.95)分,饮食管理得分为(6.23±1.41)分,容量管理较差46例,管理较好69例。单因素分析显示,不同性别、年龄、学历、工作状态、透析龄、营养不良风险的病人容量管理行为差异有统计学意义(P<0.05)。logistic回归分析结果显示,学历、透析龄、营养不良风险、自我效能、健康素养是维持性血液透析病人容量管理行为的影响因素(P<0.05)。构建列线图预测模型显示模型预测良好。结论:维持性血液透析病人容量管理行为受多种因素影响,临床可通过预测模型进行预警筛查,对学历较低的、透析龄超过5年以上的、有营养不良风险的、自我效能得分较低的、健康素养差的病人需多关注,进行多种形式和有针对性的干预措施,以提高容量管理行为。

Objective:To investigate the current status of capacity management behavior in maintenance hemodialysis(MHD)patients and explore related influencing factors.Methods:A total of 115 patients′clinical data were collected,and questionnaire data were collected from patients,including general information,capacity management behavior,self-efficacy,health literacy,etc.The current status of capacity management behavior in MHD patients and the influencing factors of capacity management behavior in MHD patients were analyzed,and a capacity management behavior prediction model was established.Results:The total score of capacity management behavior in 115 MHD patients was(18.47±2.66)points.The score for blood dialysis related indicators was(12.24±1.95)points,and the score for dietary management was(6.23±1.41)points.There were 46 cases with poor volume management and 69 cases with good management.Univariate analysis showed that there were statistically significant differences in capacity management behavior among patients of different genders,ages,educational backgrounds,work statuses,dialysis ages,and malnutrition risks(P<0.05).The logistic regression analysis results showed that education,dialysis age,malnutrition risk,self-efficacy,and health literacy were the influencing factors of capacity management behavior in MHD patients(P<0.05).The construction of a nomogram prediction model showed that the model predicted well.Conclusions:The capacity management behavior of MHD patients is influenced by multiple factors.Clinical prediction models can be used for early warning screening.Patients with low education,dialysis experience of more than 5 years,risk of malnutrition,low self-efficacy scores,and poor health literacy need to be given more attention,and various forms of targeted intervention measures should be taken to improve capacity management behavior.